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ESRM 450 Animal Movement and Home Range Lab Brian Kertson and John Marzluff We will explore the Hawth’s Tools extension for ArcMap that allows us to investigate and characterize the movement of animals. If you need an ArcMap refresher, we suggest: http://courses.washington.edu/gis250/main/schedule.html The basis of our analysis is a file of point locations from tracking an animal (in our case cougars in Snoqualmie Forest). We are interested in first learning if their movements are random or directional and second relating their movements to properties of the forest they live within.
- Getting Data into ArcMap and exploring a typical data file from a GPS-collared animal. Click on Add Data Button: Navigate to cd drive (usually the D drive), double click on cougar gps telemetry files (f136_2007_esrm_450 and m323_2007_esrm_450) Note: ArcMap will only import comma delimited (.csv), database (.dbf), and tab delimited (.txt) files for display. Excel workbooks (.xls) must be saved in one of these formats. Make sure there are no spaces in the file name or column headings as ArcMap will not allow you to import the file. You will notice you are now on the Source tab (lower left corner): Right click on the .csv file, scroll down to "Display XY data", for X Field select "EASTING", for Y Field select "NORTHING" (red arrow) Before the data is displayed, assign a coordinate system and projection. Click on "Edit" (green arrow)
Select >Projected Coordinate System > Utm > WGS 1984 > WGS 1984 UTM Zone 10N.prj > click OK Click "OK" again, data should display with name: "f136_2007_esrm_450.csv Events" Note: It is critical you know what datum and coordinate system your data was collected in. The use of the incorrect datum or projection will result in significant spatial errors in the display and analysis of the data. For a more detailed discussion of datum and projection systems click on this link (copy and paste link into browser): http//:courses.washington.edu/esrm590/lessons/projection/index.html 2) Investigate the cougar gps telemetry files: Right click on the events table > Open Attribute Table: What are the various fields? COUGAR_ID: the unique cougar identification number LINE_NO: the gps location number UTC_DATE: Date UTC_TIME: Time EASTING: the UTM east/west coordinate NORTHING: the UTM north/south coordinate HEIGHT: the elevation of the gps location, estimated by the gps satellites (often not accurate) DOP: (Positional) Dilution of Precision, a measure of the quality of the gps fix (1 is highest quality) NAV: 2D or 3D; another measure of the quality of the gps fix (3D is better than 2D) VALIDATED: yes, no; another measure of gps fix quality SATS_USED: the number of satellites used in the fix MAIN: the charge of the main collar battery BACK: the charge of the backup collar battery TEMP: air temperature next to the animal in degrees Celsius
3. Describe and Characterize Movements Hawth’s Tools>Animal Movement>Calculate Movement Parameters Enter your point file And the field that identifies the animal of interest Use Web Help for more details If locations are sorted chronologically, then you get a quick calculation of lengths and turn angles between each location. These are added point by point to the attribute table (see next page). These can be summarized quickly by clicking on the column in the attribute table, right clicking, and then clicking on statistics (see below). Attribute table with path variables added Statistics of a column 3.Consider the Randomness of Movements Hawth’s Tools>Animal Movement>CRW Simulation Tools Use the simple option and then use the first UTM coordinate for a cougar as the starting point, do a pure random walk. See how this walk compares to the observed track of the cougar.
Can any of these results be applied to your research questions or do they suggest new questions? Look at attribute table of the random walk. How does it compare to the actual walk? Think about how you could use these analyses to investigate resource selection.ra For Advanced Users: Explore other statistics in ArcMap that would be useful. Spatial Statistics Tool>Analyzing Patterns>Average Nearest Neighbor Spatial Statistics Tool>Measuring Geographic Distributions>Linear Directional Mean
Home range estimation is possible within Arcmap using the Hawth’s Tools extension.
1. Determining the home range of cougars. Your starting layers are the point locations of male and female cougars. Calculate the Minimum Convex Polygon home range Hawth’s Tools>Animal Movements>Create minimum convex polygon
Calculate the Fixed Kernel home range estimate First, we need to estimate h, the smoothing factor or range of spatial dependence in your data. The HRT tool in ArcMap is unreliable, so we need to use ArcView at this point. For our purposes we also need a smaller random sample of points to estimate the smoothing factor. This can be done within ArcView, using the extension Animal Movements. Open Arc View and use your cougar shape file of points. Movement>Random Selection>0 (to take a set # of points)>300>NO (without replacement) With the selection in force (yellow points are those 300 you selected) determine h using Least Squares Cross Validation Home Range>Kernel, use the default LSCV technique Write down the h value and finish the run
2. Overlap in home range. There are two ways to compare the overlap in space use by animals. To illustrate these methods use the male and female cougar from Snoqualmie, who share a bit of their ranges. A. Calculate the 2-dimensional overlap – the amount of space shared by the cats assuming each spot in a home range is used equally. Hawth’s Tools>Analysis Tools>Polygon in Polygon Analysis When the analysis is run a new field is added to the Zonal Polygon (the female cougar’s mcp in my example (you enter the other animal of interest in the Summary Polygon Layer (the male in my example). Open the attribute table of the zonal polygon shape file and the area of overlap is the new variable and the total area is the “Area” variable. The proportional, 2-dimensional overlap is simply the area of overlap / total area of range (0 in my example).
B. Calculate the 3-dimensional overlap—the amount of space shared by the cougars assuming unequal use within the home range. Before you can calculate the 3-dimensional overlap of the male and female cougar, the kernels must be converted from a kernel density estimate (does not sum to 1) to a true probability density function (sums to 1) Open Hawth's Tools > Sampling Tools > Generate Regular Points Click OK and you will see the prompt below asking if you would like to center the first point in the center of the cell in the upper left corner, click Yes. The new point shapefile will be generated, this may take a couple of minutes. Specify the layer in which the point layer's extent will be based = the kernel Make sure the Point Spacing is the same as the cell size of the raster (30 m) and is locked into a 1:1 ratio (default option) Name the point grid shapefile and specify the destination where it will be saved
You will now need the determine the sum of cougar kernel to convert the units of the kernel from density to volume. Hawth's Tools > Analysis Tools > Intersect Point Tool (this will add a field with the kernel value for each point) Click OK, the analysis will take several minutes. Right Click on the point grid shapefile > Open Attribute Table. You will see a new field with the abbreviated name of your raster. Right Click on the field > Statistics (same as before) Write down the sum value The point grid shapefile that the kernel values will be appended too too. The raster from which the values will be extracted New field with value from raster
Repeat these steps for the other cougar. To convert the kernel density estimate to a probability density function, you will need the Raster Calculator within the Spatial Analyst Toolbar: Tools > Extensions > check box for Spatial Analyst (dock the new toolbar in the menu) Spatial Analyst > Raster Calculator > enter equation: "kernel_name / sum" > Evaluate This will create a new raster identical in appearance and characteristics, but with converted values (ArcMap will assign the name Calculation, you can rename the raster whatever you would like) Note: If you summed the values of this new raster it would be 1, a true PDF.
To get the total Volume of Intersection (VI = 3d overlap) you will need the sum of the minimum use raster you just created which requires you to follow the same steps you performed to get the sum of the kernel. Create a point grid: Hawth's Tools > Sampling Tools > Generate Regular Points (follow previous steps) Append the raster values to the point grid: Hawth's Tools > Analysis Tools > Intersect Point Tool (follow steps from above) -This will take a few minutes, be patient Right click on your point grid shapefile > Open Attribute Table > right click on the new field > Statistics: Sum = Volume of Intersection (3d overlap) OVERLAP QUESTION : How do the 2D and 3D overlap estimates compare? Literature Cited Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24:346-352. Hooge, P. N., and B. Eichenlaub. 1997. Animal movement extension to Arcview: version 1.1. Alaska Biological Science Center, U.S. Geological Survey, Anchorage, Alaska, USA. Kernohan, B. J., R. A. Gitzen, and J. J. Millspaugh. 2001. Analysis of animal space use and movements. Pages 126-166 in J. J. Millspaugh and J. M. Marzluff, editors. Radio Tracking and Animal Populations. Academic Press, Inc., San Diego, California, USA. Ostro, L. E. T., T. P. Young, S. C. Silver, and F. W. Koontz. 1999. A geographic information system method for estimating home range size. Journal of Wildlife Management 63:748-755. Samuel, M.D. and M. R. Fuller. 1996. Wildlife radiotelemetry. Pp. 370-418. In: T.A.Bookhout (ed.) Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Md. Seaman, D. E., and R. A. Powell. 1996. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77:2075-2085. Seaman, D. E., J. J. Millspaugh, B. J. Kernohan, G. C. Brundige, K. J. Raedeke, and R. A. Gitzen. 1999. Effects of sample size on kernel home range estimates. Journal of Wildlife Management 63:739-747. van Winkle, W. 1975. Comparison of several probabilistic home-range models. Journal of Wildlife Management 39:118-123. White, G. C. and R. A. Garrott. 1990. Analysis of wildlife radio-tracking data. Academic Press, Inc., San Diego, California, USA. Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home- range studies. Ecology 70:164-168.
